fix style

Using the following YAPF style
========================================================
based_on_style = pep8
ALIGN_CLOSING_BRACKET_WITH_VISUAL_INDENT=True
BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF=False
COLUMN_LIMIT=100
SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET=False
SPLIT_ARGUMENTS_WHEN_COMMA_TERMINATED=True
SPLIT_BEFORE_FIRST_ARGUMENT=False
SPLIT_BEFORE_NAMED_ASSIGNS=False
SPLIT_PENALTY_AFTER_OPENING_BRACKET=30
USE_TABS=False
========================================================

Author: phi-dbq

python/sparkdl/param/shared_params.py

@@ -12,25 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-
 """
 Some parts are copied from pyspark.ml.param.shared and some are complementary
 to pyspark.ml.param. The copy is due to some useful pyspark fns/classes being
 private APIs.
 """
-
+import textwrap
 from functools import wraps
 
 from pyspark.ml.param import Param, Params, TypeConverters
 
 from sparkdl.param.converters import SparkDLTypeConverters
 
-
 ########################################################
 # Copied from PySpark for backward compatibility.
 # They first appeared in Apache Spark version 2.1.1.
 ########################################################
 
+
 def keyword_only(func):
     """
     A decorator that forces keyword arguments in the wrapped method
@@ -54,8 +53,8 @@ class HasInputCol(Params):
     Mixin for param inputCol: input column name.
     """
 
-    inputCol = Param(
-        Params._dummy(), "inputCol", "input column name.", typeConverter=TypeConverters.toString)
+    inputCol = Param(Params._dummy(), "inputCol", "input column name.",
+                     typeConverter=TypeConverters.toString)
 
     def setInputCol(self, value):
         """
@@ -75,8 +74,8 @@ class HasOutputCol(Params):
     Mixin for param outputCol: output column name.
     """
 
-    outputCol = Param(
-        Params._dummy(), "outputCol", "output column name.", typeConverter=TypeConverters.toString)
+    outputCol = Param(Params._dummy(), "outputCol", "output column name.",
+                      typeConverter=TypeConverters.toString)
 
     def __init__(self):
         super(HasOutputCol, self).__init__()
@@ -94,6 +93,7 @@ def getOutputCol(self):
         """
         return self.getOrDefault(self.outputCol)
 
+
 ########################################################
 # New in sparkdl
 ########################################################
@@ -196,8 +196,7 @@ class HasOutputMapping(Params):
     """
     Mixin for param outputMapping: ordered list of ('outputTensorOpName', 'outputColName') pairs
     """
-    outputMapping = Param(Params._dummy(),
-                          "outputMapping",
+    outputMapping = Param(Params._dummy(), "outputMapping",
                           "Mapping output :class:`tf.Operation` names to DataFrame column names",
                           typeConverter=SparkDLTypeConverters.asTensorNameToColumnMap)
 
@@ -212,8 +211,7 @@ class HasInputMapping(Params):
     """
     Mixin for param inputMapping: ordered list of ('inputColName', 'inputTensorOpName') pairs
     """
-    inputMapping = Param(Params._dummy(),
-                         "inputMapping",
+    inputMapping = Param(Params._dummy(), "inputMapping",
                          "Mapping input DataFrame column names to :class:`tf.Operation` names",
                          typeConverter=SparkDLTypeConverters.asColumnToTensorNameMap)
 
@@ -228,9 +226,15 @@ class HasTFHParams(Params):
     """
     Mixin for TensorFlow model hyper-parameters
     """
-    tfHParams = Param(Params._dummy(),
-                      "hparams",
-                      "instance of :class:`tf.contrib.training.HParams`, a key-value map-like object",
+    tfHParams = Param(Params._dummy(), "hparams",
+                      textwrap.dedent("""\
+                      instance of :class:`tf.contrib.training.HParams`, a namespace-like
+                      key-value object, storing parameters to be used to define the final
+                      TensorFlow graph for the Transformer.
+
+                      Currently accepted values are:
+                      - `batch_size`: number of samples provided to the inference graph
+                                      during each evaluation function call.""",
                       typeConverter=SparkDLTypeConverters.toTFHParams)
 
     def setTFHParams(self, value):